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Abstract During 2019 to 2022, a comprehensive study was conducted across 71 stations spanning over 15 states in India to investigate air quality variations during the pre-Diwali, Diwali, and post-Diwali periods. The investigation, centred on the extensive use of firecrackers during festive times, revealed a short-term deterioration in air quality with long-lasting impacts. PM2.5 and PM10 levels were found to significantly increase during Diwali and the post-Diwali period compared to the pre-Diwali period. The levels of PM2.5 and PM10 experience respective increases of 44.66% and 11.03%, 29.12% and 05.41%, 52.63% and 23.38%, and 39.65% and 17.61% during Diwali period from 2019 to 2022, where 68.84% and 34.32%, 44.42% and 29.42%, and 48.70% and 25.22% in 2019, 2021 and 2022 in post-Diwali period compared to the pre-Diwali. The Mann–Kendall and Sen's slope analyses reveal statistically significant positive trends (increasing) in both PM10 and PM2.5 levels during the Diwali festival period. Notably, the study introduced the Health Hazard-based Air Quality Index (HAQI), which demonstrated higher values than the National Ambient Air Quality Index (NAAQI). Unlike NAAQI, which considers individual pollutant sub-index values, and considered highest one of sub-index values in AQI analysis. HAQI provides a more comprehensive assessment of health risks associated with exposure to multiple pollutants effects. In 2022, AQI and HAQI values during pre-Diwali were 114 and 146.18, during Diwali were 173 and 332.18, and post-Diwali were 191 and 414.14, respectively. These findings underscore the elevated risk during Diwali and the post-Diwali period due to heightened pollution levels, emphasizing the need for public awareness and potential mitigation strategies. The study calls attention to the importance of considering cumulative health impacts and suggests the adoption of HAQI for more informative air quality assessments.

Abstract The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.

This study was designed to investigate the toxic effects of benzo (a) pyrene (BaP) in the lungs. Mice were repeatedly treated orally with BaP (50 mg/kg body weight, twice a week for four weeks) to induce a tumour. After 4 months of BaP administration, tumours were visible beneath the skin. The histopathological section of the lungs shows congestion of pulmonary blood vessels, alveolar hyperplasia, and concurrent epithelial hyperplasia with infiltrates of inflammatory cells also seen. Thereafter, a single-cell suspension of lung tissues was stained with fluorescently conjugated antibodies for the demarcation of alveolar epithelial (anti-mouse CD74 and podoplanin) and macrophage (F4/80 and CD11b) cells and measured by flow cytometry. The expression of antioxidant genes was assessed by qRT–PCR. The number of alveolar epithelial cells 1 (AEC1) increased, but the number of alveolar epithelial cells 2 (AEC2) and transitional alveolar epithelial cells (TAEC) was significantly decreased in tumour-bearing mice. The proportion of CD11b+ alveolar macrophages (AM) and interstitial macrophages (IM) was increased, but the proportion of F4/80+ AM cells was reduced. The BaP administration significantly increased the ROS production in alveolar cells. The relative expression levels of antioxidant genes (SOD1, catalase, GPX1, and HIF-1α) were increased, but NRF2 expression was decreased in BaP-treated alveolar cells. The expression of anti-inflammatory (NF-κB) was also significantly increased. In conclusion, BaP exposure induced an inflammatory response, altered alveolar epithelial cell and macrophage diversity, and increased antioxidant responses in the lungs.

Fungal endophytes are reservoirs of bioactive molecules that impart therapeutic potential to indigenous medicinal plants. Since new strains of microbial pathogens are evolving with their ability towards drug resistance, herbal phytometabolites are a new lead for exploiting their potential in the field of medicine. The ethnobotanical literature and scientific potential of medicinal plants in traditional medicine have always developed interest in the piqued for new bioactive compounds produced by endophytic bacteria and fungi. The endophytic association of medicinal plants imparts therapeutic attributes to the plant metabolites. Endophytes include bacteria, viruses, and fungi, but as per the literature, fungal endophytes are more promising for exploring the potential compounds, they synthesize in host plants. The occurrence of endophytic associations is found in almost all plant species, and their roles are well established. This association is present in roots, stems, and leaves, these microbial endophytes enhance growth, yield, stress tolerance, control plant pathogens, and manage diseases naturally. In the present review, the potential benefits of endophytes such as anti-inflammatory, antimicrobial, anticancer, antidepressants, immunosuppressant, etc. are discussed.

Abstract This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models—RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models.

Next generation sequencing (NGS) technology offers new opportunities in biological research. In the last few decades, remarkable technological innovations have emerged, allowing the exploration of unexplored areas of functional genomics and biomedical research at a cost-effective and unprecedented scale and speed. Along with such revolutionary advances came new challenges in methodologies and informatics. Despite not being directly engaged in NGS methods, chromatography's inclusion makes the sequencing process less error prone. These advanced technologies offered novel and rapid ways to sequence static genomes as well as entire transcriptomes for expression analysis under different conditions. Utilization of next-generation sequencing has the potential to entirely rewrite how biological research is conducted and chromatography plays a crucial role in this process. New technologies are evolving rapidly, and addressing prospective issues like the amelioration of protocols for generating sequencing libraries, offering new strategies for data analysis, and most importantly, restructuring and revamping experimental design. In this review, the emergence and evolution of deoxyribonucleic acid (DNA) sequencing techniques from the classical Sanger DNA sequencing method to modern next generation technologies are reviewed, and their applications in-conjugation with chromatographic and spectrometric techniques in modern biological studies are discussed.

This paper presents two approximations of the solution functions of Abel’s integral equations belong ing to classes Hα[0,1), Hϕ[0,1) by (λk+1 −1,M)th partial sums of their second kind Chebyshev wavelet expansion in the interval [0,1), for λ > 1. These approximations are E(1) λk+1−1,M (f), E(2) λk+1−1,M (f). Chebyshev wavelets of the second kind were used to solve Abel’s integral equations. The Chebyshev wavelet of the second kind leads to a solution that is almost identical to their exact solution. This research paper’s accomplishment in wavelet analysis is noteworthy.

Predicting FRP-to-concrete bond strength (FRP-CBS) under diverse exposure conditions is an intricate task influenced by multiple variables. Yet, existing pertinent models have several limitations. Accordingly, this study proposes a novel data driven machine learning (ML) methodology to predict the FRP-CBS considering various exposure conditions. A comprehensive database on single and double lap-shear strength tests on concrete specimens was meticulously compiled. Twenty-seven analytical models were used to appraise the developed ML models. Feature importance analysis was conducted to ascertain the influence of input parameters on bond strength. The proposed data-driven ML models attained exceptional accuracy and superior performance compared to existing analytical models. To enhance the accuracy of bond strength estimation and simplify the process for practicing engineers and FRP applicators, a user-friendly graphical interface was developed. It could eliminate the need for complex design procedures, making it easier to accurately estimate the FRP-CBS, thus improving overall efficiency in engineering practice.

Background: Women with adnexal masses still pose a diagnostic dilemma and require efficient triaging. The Risk of Malignancy Index (RMI) is an algorithm used for differentiation of malignant from benign ovarian masses. Aims and Objectives: The present study aimed to evaluate the four indices RMI 1, RMI 2, RMI 3, and RMI 4 to discriminate a malignant ovarian tumor from a benign one. Materials and Methods: This cross-sectional study was carried out at a tertiary care hospital of Eastern Uttar Pradesh, India, in 312 patients with ovarian masses. RMI scores were calculated based on pre-operative cancer antigen-125 levels, ultrasonography, and menopausal status followed by post-operative histopathology, taken as gold standard. Using cutoff points of RMI indices, the sensitivity, specificity, positive predictive value, and negative predictive value of RMI were assessed. Results: Out of total 312 patients, 68 (21.8%) had malignant disease and 244 (78.2%) had benign pathology. All indices presented a significantly better performance in detecting malignancy than the use of a single parameter. Each of the RMIs had a different optimal threshold; however, using a threshold of 250, RMI 1, RMI 2, RMI 3, and RMI 4 had a sensitivity and specificity of 64.7%, 70.5%, 64.7%, and 65.2% and 93.4%, 83.6%, 90.2%, and 91.4%, respectively. Conclusion: The RMI is a simple, composite tool that at lower values can reliably predict benign nature of adnexal tumors. However, at values above the cutoff points, it is helpful for appropriate surgical planning and averts radical surgeries in women of reproductive age group.

Abstract Background Most of the scoring systems to predict difficult laparoscopic cholecystectomy are based on pre-operative clinical and radiological findings. Recently the Parkland Grading Scale system was introduced as a simple intra-operative grading scale. This study aims to utilize the Parkland Grading Scale system to assess the intraoperative challenges during laparoscopic cholecystectomy. Method This was a prospective, cross-sectional study done at Chitwan Medical College and Teaching Hospital, Chitwan, Nepal. All the patients underwent laparoscopic cholecystectomy from April 2020 to March 2021. Based on the initial intra-operative finding, Parkland Grading Scale was noted and at the end of the surgery, the level of difficulty was given by the operating surgeon. All the pre-operative, intra-operative, and post-operative findings were compared with the scale. Results Out of 206 patients, there were 176 (85.4%) females, and 30 (14.6%) males. The median age was 41 years (Range 19–75). The median body mass index was 23.67 kg/m2. There were 35(17%) patients with a history of previous surgery. The rate of conversion to open surgery was 5.8%. According to Parkland Grading Scale, 67(32.5%), 75(36.4%), 42(20.4%), 15(7.3%), and 7(3.4%) were graded as grade 1, 2, 3, 4, and 5 respectively. There was a difference in the Parkland grading scale in patients with a history of acute cholecystitis, gallbladder wall thickness, pericholecystic collection, stone size, and body mass index (p

Abstract The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

Introduction Chronic lymphocytic leukemia (CLL) is the commonest hematological malignancy in the West but is relatively uncommon in India. The prognosis of CLL is determined by well-established prognostic markers. CD49d has been emerging as a promising prognostic marker in CLL. CD49d expression in CLL has been found to have an aggressive clinical course, shorter time to first treatment, and poorer prognosis. The aim of this study was to analyze the flow cytometric expression of CD49d in newly diagnosed CLL and to correlate its expression with clinico-hematological parameters. Materials and Methods Twenty-five consecutive patients of CLL, diagnosed on flow cytometry, were included in the study. Patients on treatment or those with relapse were excluded. The panel for flow cytometry included the routine markers used for CLL diagnosis along with CD49d. The expression of CD49d was correlated with clinico-hematological parameters in all patients. “R” software was used for the statistical analysis. Fisher's exact test and Wilcox test were used to assess the correlation of CD49d to categorical and continuous data, respectively. Results The mean age of the patients was 62.6 ± 12.5 years, and 80% were symptomatic at diagnosis. CD49d expression was found in 44% cases, with a higher proportion being male patients. CD49d and prolymphocyte percentage showed a statistically significant correlation (p = 0.0007). We found a statistically significant correlation between CD49d expression and lymphadenopathy and splenomegaly with p-values of 0.033 and 0.0472, respectively. CD49d positivity correlated significantly with a higher Rai stage (p = 0.0196) and intermediate and high-risk cases according to Binet staging (p = 0.033). Conclusion CD49d expression in the present study correlated with a higher prolymphocyte percentage, lymphadenopathy, splenomegaly, and higher Rai and Binet stages. CD49d expression on flow cytometry was reproducible and easy to interpret.

Let Fq be any finite field of characteristic p>0 having q = pn elements. In this paper, we have obtained the complete structure of unit groups of group algebras Fq[QD2k], for k = 4 and 5, for any prime p>0, where QD2k is quasidihedral group of order 2k.

The emerging lung pathogen Mycobacterium abscessus is understudied for its virulence determinants and molecular targets for diagnosis and therapeutics. Here, we report a comprehensive secretome (600 proteins) of this species, which was identified using a multipronged strategy based on genetic/genomic, proteomic, and bioinformatic approaches. In-solution digested bottom-up proteomics from various growth phases identified a total of 517 proteins, while 2D-GE proteomics identified 33 proteins. A reporter-gene-fusion-based genomic library that was custom-generated in this study enabled the detection of 23 secretory proteins. A genome-wide survey for N-terminal signal sequences using bioinformatic tools (Psortb 2.0 and SignalP 3.0) combined with a strategy of the subtraction of lipoproteins and proteins containing multiple transmembrane domains yielded 116 secretory proteins. A homology search against the M. tuberculosis database identified nine additional secretory protein homologs that lacked a secretory signal sequence. Considering the little overlap (80 proteins) among the different approaches used, this study emphasized the importance of using a multipronged strategy for a comprehensive understanding of the secretome. Notably, the majority of the secreted proteins identified (over 50%) turned out to be “orphans” (those with no known functional homologs). The revelation of these species-specific orphan proteins offers a hitherto unexplored repertoire of potential targets for diagnostic, therapeutic, and vaccine research in this emerging lung pathogen.

In regions like Nepal, characterized by diverse geography, missing weather data poses a significant challenge for traditional imputation methods. These methods often struggle to capture the complexities of dynamic environments adequately. To overcome this challenge, our study explores the application of graph neural networks for weather prediction in data-scarce environments. Our approach entails the development of specialized models tailored to accommodate the non-Euclidean topology inherent in weather data. This framework encompasses preprocessing, graph representation, feature selection, and imputation techniques to predict missing atmospheric variables. The adaptability of our models to intricate geography ensures more precise representations of weather conditions. Our research demonstrates the efficacy of these models through rigorous testing on a substantial dataset spanning four decades since 1981. By harnessing state-of-the-art graph neural network technology, our study aims to address existing gaps in weather data prediction, leading to improved historical weather forecasting accuracy. Ultimately, this advancement contributes to enhanced meteorological understanding and forecasting precision in data-scarce regions.

Background: Dexmedetomidine now has become one of the frequently used drugs as part of a multimodal analgesic regimen to provide improved pain control at all perioperative stages for laparoscopic surgeries. Aims and Objectives: In this prospective, double-blind, randomized, and control trial, we compared the postoperative analgesic efficacy of intravenous (IV) dexmedetomidine and intraperitoneal (IP) dexmedetomidine in laparoscopic cholecystectomy. Materials and Methods: Sixty patients of the American Society of Anesthesiologists Grade I and II undergoing laparoscopic cholecystectomy were allocated into two groups. The patients in Group-IV had received 30 ml of 0.5 μg/kg Dexmedetomidine infusion intravenously plus 40 ml of Ropivacaine (30 mL of 0.5% Ropivacaine and 10 ml NS) intraperitoneally and in Group-IP received 30 ml NS intravenously and 40 ml of Ropivacaine plus Dexmedetomidine (30 mL of 0.5% Ropivacaine and 0.5 μg/kg Dexmedetomidine diluted in 10 ml NS) intraperitoneally after removal of the gallbladder. Patients were assessed during the first 24 h postoperatively for time to the first requirement of analgesia, total analgesic consumption, sedation, hemodynamics, side effects, and patient satisfaction. Results: Time to first request of analgesia (min) was longer (216.46±42.19 vs. 108.03±48.77) and total analgesic consumption (mg) was lower (92.50±32.26 vs 115.0±38.056) in Group-IV than in Group-IP. A significant difference was observed in visual analogue score in Group-IV at 1 h, 2 h, 4 h postoperatively and in the patient satisfaction score (P=0.024) in comparison to Group-IP. Conclusion: The postoperative analgesic effects and patient satisfaction, of low dose IV Dexmedetomidine plus IP instillation of Ropivacaine, are superior to low dose IP Dexmedetomidine added to Ropivacaine.

Structural elements are subjected to different types of loads, one of which is a torsional load. Due to the complexity of the analysis, torsion was not given much importance in earlier days. With stringent updates in codal provisions and due to architectural modifications, torsion is now considered one of the major parameters for structural design. The main aim of this paper is to analyze distressed elements due to torsion. It highlights different approaches, such as destructive and non-destructive processes, to be adopted to estimate the torsional parameters of a ferrocement “U” wrapped beam. The destructive method is the experimental determination of parameters, which is absolutely necessary. The non-destructive method includes an analytical method based on a softened truss model as well as a soft computing method. The soft computing method is based on the regression coefficient analysis method along with two recent optimization algorithms, i.e., (1) ARO (artificial rabbits optimization) and (2) DAOA (dynamic arithmetic optimization algorithm). The predicted results are found to be in agreement with the experimental values (destructive method). Lastly, the obtained results from both proposed methods are analyzed, and it is found that both algorithms can be utilized in any engineering problem to determine the global optimum value with corresponding input optimal settings. As the experimental method is time-consuming and expensive, analytical, and soft computing methods can be preferred over the experimental method.

Appropriate surface modifications are required to enhance the compatibility of natural fibers with polymer matrix to develop eco-friendly composites. One of such novel plant fibers is Bauhinia vahlii (BV) stem fiber. The present work aims to analyze the potentiality of this fiber as reinforcement to substitute synthetic fibers in composites. To enhance the properties of natural fibers, various chemical treatments like alkaline, peroxide, maleic anhydride and acrylation were conducted on BV fibers. Scanning electron microscope (SEM) and Fourier transform infrared spectroscopy (FTIR) were conducted to inspect the effects of chemical treatment on the morphology and composition of fibers, respectively. The crystallinity indices were calculated from X-ray diffraction (XRD) analysis. The improved tensile strength of the fiber after chemical treatment decides its usability as reinforcement in composites. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were also conducted on treated fiber to investigate its thermal stability.

Bikes are becoming an increasingly popular and reliable mode of transportation in developing countries because of their efficiency and ability to navigate through rough terrain and narrow roadways. Bikes are more vulnerable to road accidents and their riders' safety is the main concern at present days. Hence, it is essential to reduce the possibility of accidents caused by bike riders. The main reason for bike accidents is bike rider behaviours in the form of traffic rules violations. The paper's main aim is to categorize the importance of seven attributes on traffic rules violations, including bike rider behaviours, road features, ambient conditions, driving skills, type of license, bike age/tenure and riding without a safety device (helmet). Bike riders' violations that can lead to an accident and the impact of attributes have been analyzed using the structural equation modelling (SEM) technique. To analyze these attributes, 450 bike riders have been interviewed in Bhubaneswar, India. It has been concluded that bike rider behaviours are the most significant attribute of violations. Since most bike riders are young, with low income and education, paying more attention to their training and education before issuing a driving license is necessary. In addition, those who do not use safety devices (helmets) are more susceptible to committing violations. This relates to the lack of enough control and enforcement in developing cities. Also, it shows that the current traffic fines for not using safety devices (helmets) are not enforced enough. Finally, considering this research's outcomes can help minimize traffic rules violations among bike riders, which is a step towards safer roads. (original abstract)

Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel log ratio change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: “positive change”, “no change”, and “negative change” using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92% in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.

Over the world, there is growing worry about the corrosion of reinforced concrete structures. Structure repair, rehabilitation, replacement, and new structures all require cost-effective and long-lasting technologies. Fiber Reinforced Polymer (FRP) has been widely employed in both retrofitting existing structures and building new ones. Due to its varied qualities in reinforced concrete and masonry constructions as a repair composite material, FRP have seen a rise in use over the last decade. This material have several advantages such as high stiffness-to-weight and strength-to-weight ratios, light weight, possibly high longevity, and relative ease of usage in the field. Among all the parameters the bond between concrete and FRP composite play an important role in the strengthening of structures. However, the bond behaviour of the FRP-concrete interface is complex, with several failure modes, making the bond strength difficult to forecast, resulting in the FRP strengthened concrete structure. To overcome such kind of issues machine learning models are sufficient to forecast the bond strength of FRP-concrete. In this article Artificial Neural Network (ANN), optimized Artificial Bee Colony (ABC)-ANN and Gaussian Process Regression (GPR) algorithms are deployed to predict the bond strength. The R-value of ABC-ANN and GPR models are 0.9514 and 0.9618 respectively. This research aids researchers in estimating bond strength in less time, at a lower cost, and with less experimental work.

Objective: Analytical and post analytical phase are integral part of total quality management system and include steps from submission of slides till reports are dispatched. The present study was conducted to analyze the analytical and post analytical phase of the ISO15189:2012 certified cytopathology laboratory. It was also intended to study the various errors which were encountered and steps taken to reduce these discrepancies. Methods: The study included all documents of quality program from 1 November 2014 till 31 Oct 2019 in medical institute situated in north Himalayan region of India. All the data was recorded and analyzed for analytical and post analytical phase. Results: The number of samples received in cytopathology lab was 21,566 with total quality errors of 5.19%. Out of these pre-analytical errors were 55%, analytical 10.5% and post analytical errors constituted 34.4%. The maximum errors detected were due to typographical errors followed by delayed turnaround time. Cyto-histopathological discordance was 10.5% in non-gynecological cases and 2.2% in gynecological cases. Conclusion: Analytical and post analytical phase analysis is essential to minimize the errors and improve the quality of cytopathology lab. Cyto-histopathological correlation is valuable for continuous data tracking in the cytopathology with analytical errors analysis. Maintenance of external, internal quality program, turnaround time with documentation, continuous training and communication with clinician is fundamental for quality improvement in any cytopathology lab. Acknowledgement of nonconformance with root cause analysis and sincere efforts to minimize them is the basic key for successful quality management.

Aims and Objectives: Haematological malignancy may show varied presentation in different regions of world, however, Uttarakhand lacks any recent data about them. The present study was therefore conducted to study the pattern of haematological malignancies on bone marrow examination in institute of Uttarakhand. Materials and Methods: This study was conducted at All India Institute of Medical Sciences, Rishikesh, Uttarakhand, over a period of 3 years. It included patients of haematological malignancies diagnosed on bone marrow examination. Patient's age, sex, clinical history and bone marrow diagnosis were noted for every case. All the data were entered into the excel sheet and statistically analysed. Results: The study included total 256 cases of haematological malignancies with male:female ratio of 1.13:1 and mean age of 39.3 years. Chronic myeloproliferative neoplasm (CMPN) was the most common haematological malignancy (49%) with chronic myeloid leukaemia (CML) being its most common type (40%). All the haematological malignancies showed male preponderance except for non-Hodgkin's lymphoma. Mean age of CMPN was 45.81 years and of acute myeloid leukaemia was 36.92 years. Bone marrow aspirate (BMA) and trephine biopsy (BMT) together were able to diagnose 81.3% of cases of haematological malignancies. Conclusion: The study concludes that haematological malignancies present at lesser age with male preponderance in Uttarakhand. CML is the most common haematological malignancy followed by acute lymphoblastic leukaemia. BMA and BMT are complimentary to each other for diagnosis of haematological malignancies and should be used together to avoid missing of any case.

Background: Bone marrow metastasis is an uncommon yet significant presentation of many non haematological malignancies. Aims: The present study was conducted with the aim of analysing the clinical, haematological and biochemical parameters of the patients with bone marrow metastases and determines the parameters which could possibly indicate the presence of bone marrow metastases. Patients and Methods: Bone marrow aspirate, imprint and biopsy slides of 22 cases with bone marrow metastases were reviewed and the haematological and biochemical data of all these cases were compared with that of 20 controls without any evidence of metastases. Results: The mean values of mean platelet volume (MPV), neutrophil to lymphocyte ratio (NLR) and serum lactate dehydrogenase (LDH) were found to be significantly different (P < 0.001) between the cases and controls. MPV 3.5 and LDH >452 U/L showed a high likelihood ratio in predicting bone marrow metastases. Conclusion: Parameters such as MPV, NLR and LDH can be used as efficient and easily available methods to predict bone marrow metastasis for a rapid diagnosis.

The contribution of plastic packaging in food is substantial, but issues associated with its biodegradability and recycling initiates major concerns for environment and human health thus necessitating substitution of conventional synthetic plastics with bio-based packaging materials. Studies have demonstrated economic competition of bio-based materials with synthetic plastics because of their biodegradability, wide availability, excellent barrier and mechanical properties. These materials can be commercialized on a large scale but still somehow are under-investigated in many areas. Moreover, some of the influencing factors including confusion between bio-based and biodegradability pose a significant challenge on evolution of bio-based packaging. Therefore, the review focuses on bio-based packaging materials derived from various renewable resources like biomass, microorganisms and microalgae involving their possible food packaging applications along with required development in multiple regions for proper utilization of offered advantages in the food packaging market.

Cement manufacturing and utilization is one of the majorly responsible factors for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material’s strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864, and 0.9545, and 1.6471 MPa, 2.7149 MPa, and 4.4334 MPa, respectively. The results obtained from this study have been found to be promising and enrich the available literature. This work will nudge civil engineering and material science researchers toward opting for sustainable computing techniques. However, the study’s limitations include the need for additional research into the material’s long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.

Corrosion of embedded steel reinforcement is the prime influencing factor that deteriorates the structural performance and reduces the serviceability of reinforced concrete (RC) structures, especially during earthquakes. In structural elements, RC columns play a vital role in transferring the superstructure’s load to the substructure. The deterioration of RC columns can affect the structures’ overall performance. Hence, it becomes essential to estimate the remaining life of deteriorated RC columns. In the literature, only limited analytical models are available to calculate the remaining life of corroded and eccentrically loaded RC columns. As the number of dependent parameters increases, assessing the residual life of the structural elements and providing a practically applicable suitable model become very complex. Machine learning (ML)-based prediction models are beneficial in dealing with such complex databases. In this article, an ML-based artificial neural network (ANN), Gaussian process regression (GPR), and support vector machine (SVM) algorithms have been applied to estimate the residual strength of corroded and eccentrically loaded RC columns. The performance of the analytical and ML models is accessed using commonly used performance indices, namely, the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), a-20 index, and Nash–Sutcliffe (NS). The results of the proposed ANN model have been compared with the existing analytical model to identify the suitability of the best model. Based on performance analysis, the precision of the GPR and SVM models is lower than that of the ANN model. The processed results revealed that the R2 value of the ANN model for training, testing, and validation datasets is 0.9908, 0.9757, and 0.9855, respectively. The MAPE, MAE, RMSE, NS, and a-20 index for all the datasets are 8.31%, 48.35 kN, 72.53 kN, 0.9886, and 0.8978, respectively. The precision of the ANN model in terms of the coefficient of determination is 225.77% higher than that of the analytical model. The sensitivity analysis demonstrates that the compressive strength of concrete plays the most significant role in the load-carrying capacity of corroded and eccentrically loaded RC columns. The proposed ANN model is reliable, accurate, fast, and cost effective. This model can also be used as a structural health-monitoring tool to detect the early damages in the RC columns.

BackgroundBreast cancer is the most common type of cancer in women, and vast research is being conducted throughout the world for the treatment of this malignancy by natural products using various computational approaches. Xanthohumol, a prenylated flavonoid, is known for its anticancer activity; however, the mechanism behind its action is still in the preliminary stage.MethodsThe current study aimed to analyze the efficacy of xanthohumol compared to the currently available anticancer drugs targeting phosphoinositide-3-kinase (PI3K), serine/threonine kinase (AKT) receptors, and human epidermal growth factor receptor 2 (HER2) for breast cancer treatment through in silico analysis.ResultsThe result revealed that the target compound showed significant binding affinity to targets within the PI3K, AKT, and HER2 signaling pathways with a binding energy of −7.5, −7.9, and −7.9 kcal/mol, respectively. Further prediction studies were then made concerning this compound’s absorption, distribution, metabolism, and excretion (ADME) as well as drug-likeness properties, resulting in its oral bioavailability with only a single violation of Lipinski’s rule of five.ConclusionsThe finding revealed the ability of xanthohumol to bind with multiple cancer cell signaling molecules including PI3K, AKT kinase, and HER2. The current novel study opened the door to advancing research into the management and treatment of breast cancer.

Pyrenes are polycyclic aromatic hydrocarbons responsible for an array of health hazards and environmental abuses. The remediation of such compounds is remaining as an area of interest for scientist community. The present study aimed for isolation and screening of pyrene degrading bacterial strains optimized at various environmental and nutritional requirement to assess their potential for pyrene degradability. The result have revealed that isolated strain GWP-2 and LOP-9 showed degradation of pyrene 86% and 82.1%, respectively at temperature 37 °C and pH 9 in presence of glucose(1%) and peptone(0.5%) within 15 days of incubated at 140 rpm in temperature controlled shaker. The variation of temperature and pH from optimized condition decrease the performance of isolated strains. The metabolite characterization through GCMS showed strain GWP-2 9,12-octadecadienoic acid, Oxido-4,17-cholestadian-3a,16a-diol, Heptadecane,2,6,10,15-tetramethyl, Heptacosane, 1,2-benzenedicarboxylic acid and Octadecane 3-ethyl-5-(2-ethylbutyl) as major product while LOP-9 showed their major product as Disiloxane,hexamethyl-(CAS), Benzene -1,2,4-triol(trimethylsilyl) ester, Octahydroquinoline-6-carboxylic acid(phenylethyl amide) and 4-methyl1,4‑hydroxy-6-(2,5,6,6′-tetramethylcyclohex-1-en-1-yl)hex-2-ynyl acetate after 15 days degradation. Many compounds detected in control were completely removed. The isolated strains were identified as Mycobacterium vaanbaalenii GWP-2 (ON715011) and Staphylococcus aureus LOP-9(ON715121) based their 16s-rRNA sequencing. These potential strains may be useful for detoxification of pyrene containing pollution in soil and water ecosystem.

Background: Labour pain is among the most severe pain that a women experience in their lifetime. Since abolition of labour pain has always been surrounded by myths and controversies, providing effective and safe analgesia in labour remained a challenge. Objective: The Objective of this survey was to find out awareness, attitude and willingness to receive Labour Analgesia among pregnant women attending maternity hospitals and providing health education regarding labour analgesia to the participants and attendants encountered during study. Method: This cross-sectional survey was conducted from January to March 2022 among pregnant women attending obstetrics & gynaecology department of BRD Medical College, Gorakhpur using semi structured, pretested questionnaire. Personal interview of participants was done to collect data. Result: A total 204 valid responses were obtained out of 225 participants contacted (response rate 90.6%). Age of the participants were 20–35 years (mean 25.18, standard deviation 3.2). We found very little awareness regarding labour analgesia among participants attending to antenatal clinics but after providing information majority were willing to avail the facility of labour analgesia. Conclusion: Awareness about labour analgesia was found limited but majority had positive attitude towards it. This demand supply gap can be filled by appropriate health education and increasing awareness about availability of these services.

Fractal geometries consistently provide solutions to several electromagnetic design problems. In this paper, fractal geometries such as Hilbert and Moore curves are used to design efficient High-Impedance Surfaces. Modern communication devices have many sensors that are needed to communicate wirelessly. The critical component of wireless communications is antennas. Planar microstrip patch antennas are popular due to their low profile, compactness, and good radiation characteristics. The structural disadvantages of microstrip antennas are that they have surface waves that propagate over the ground plane. High-Impedance Surface (HIS) planes are a prominent solution to minimize and eliminate surface waves. The HIS structures behave as active LC filters that suppress surface waves at their resonance frequency. The resonance frequency of the structure is obtained by its LC equivalent or by analyzing the reflection phase characteristics. This work presents conventional HIS structures similar to mushroom HIS and fractal HIS such as Hilbert curve and Moore curve HIS. The HIS reflection phase characteristics are obtained by applying periodic boundary conditions with plane wave illumination. The results were obtained in terms of the reflection phase angle. The conventional mushroom structures show narrow band characteristics at given dimensions of 10 mm × 10 mm and 20 mm × 20 mm. These structures are helpful in the replacement of PEC ground planes for patch antennas under sub-6 GHz. The Hilbert and Moore fractals are also designed and have a multiband response that can be useful for L, S, and C band applications. Another design challenge of HIS is protrusions, which make design difficult. The work also presents the effect of having vias and the absence of vias on reflection phase characteristics. The response shows the least and no significant effect of vias under the x-band operation.

Hepatobiliary rhabdomyosarcoma (RMS) is a soft tissue malignant musculoskeletal tumor of the biliary tract. It is rare tumor, mostly seen in children and mimics common benign biliary condition. Here, we present a case of 1 year 9 months child of biliary RMS who presented with obstructive jaundice and diagnosed after biopsy. This case highlights the unusual childhood malignancy of bile duct RMS in obstructive jaundice with atypical imaging findings.

Background: The current cross-sectional study analyses patients' plasma iron measurements and spirometry to determine the relationship between serum iron, percent transferrin saturation, serum ferritin, total iron-binding capacity (TIBC), and forced expiratory flow (FEF 25–75%), forced expiratory volume 1(FEV1)/Forced vital capacity (FVC), FVC, and FEV1. Methods: Spirometry measurements were assessed by comparing reference values based on age, height, sex, and race. Descriptive analysis of data included percentages, means, and standard deviations. Multivariate analyses were done using the Pearson correlation test and the multiple linear regression method. Results: According to the findings, the average age of those who participated in the study was 59.63 ± 10.32 years old. Serum iron concentrations were shown to be extremely significant and positively correlated with the percent anticipated values of FVC, FEV1/FVC FEV1, and FEF (25–75%). The total iron-binding capacity was extremely significant and linked strongly with the percent expected values of FVC, FEV1, FEF (25–75%), and FEV1/FVC. The percent transferrin saturation was extremely significant and linked positively with the percent anticipated values of FVC, FEV1, FEF (25–75%), and FEV1/FVC. Conclusion: Plasma iron levels were independently linked with both FEV1 and FEF (25–75%) percent predicted in the current investigation. Consequently, the blood iron concentration in COPD patients can be utilized as a biomarker for assessing disease severity because it is relatively simple and inexpensive.

Clostridioides difficile infection (CDI) presents a major public health threat by causing frequently recurrent, life-threatening cases of diarrhea and intestinal inflammation. The ability of C. difficile to express antibiotic resistance and to form long-lasting spores makes the pathogen particularly challenging to eradicate from healthcare settings, raising the need for preventative measures to curb the spread of CDI. Since C. difficile utilizes the fecal–oral route of transmission, a mucosal vaccine could be a particularly promising strategy by generating strong IgA and IgG responses that prevent colonization and disease. This mini-review summarizes the progress toward mucosal vaccines against C. difficile toxins, cell–surface components, and spore proteins. By assessing the strengths and weaknesses of particular antigens, as well as methods for delivering these antigens to mucosal sites, we hope to guide future research toward an effective mucosal vaccine against CDI.

With low-power gadgets proliferating, the development of a small, effective rectenna is crucial for wirelessly energizing devices. A simple circular patch with a partial ground plane for RF-energy harvesting at ISM (2.45 GHz) band is proposed in this work. The simulated antenna resonates at 2.45 GHz with an input impedance of 50 Ω and a gain of 2.38 dBi. An L-section matching a circuit with a voltage doubler is proposed to provide excellent RF-to-DC transformation efficiency at low power input. The proposed rectenna is fabricated and the results show that the return loss and realized gain have good characteristics at the ISM band with 52% of RF-to-DC transformation efficiency, with an input of 0 dBm power. The projected rectenna is apt for power-up low sensor nodes in wireless sensor applications.

The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.

Context: Platelet-rich plasma (PRP) releases growth factors (GFs) upon activation which accelerates the healing and regeneration in the target tissue. Wider applications of PRP warrant product standardization. PRP compositions may vary according to the patient's demographics and the constituents of PRP. Aims: Our study aimed to assess the correlations between age, gender, platelet counts, platelet indices, platelet yield, platelet dose, GF dose, and platelet-derived GF-BB (PDGF-BB) concentration. Settings and Design: This was an experimental study done in the Department of Pathology along with the Department of Dentistry at a tertiary care hospital in North India. Subjects and Methods: PRP was prepared from 40 ml of whole blood. Platelet counts, platelet indices (LH 750 coulter analyzer, Florida, USA), and PDGF-BB levels (enzyme-linked immunosorbent assay, Qayee Bio., Ltd, China) were measured. Platelet yield (%), platelet dose (×106), and GF dose (×1012) in PRP were also calculated. Statistical Analysis Used: All the parameters were analyzed using Pearson's correlation coefficient and linear regression. Paired t-test and Mann–Whitney test were used for two-group analysis. Results: Mean platelet count, PDGF-BB concentration, platelet yield, platelet dose, and GF dose in PRP were 1317 × 103/mm3, 30 ± 9.89 ng/ml, 71.62 ± 28.34%, 6.5 ± 3.5 × 109, and 159.62 ± 52.39 ng/ml, respectively. PRP platelet counts were positively correlated with PDGF-BB concentration, platelet yield, platelet dose, and GF dose. Conclusions: Of all the parameters, PDGF-BB concentration, platelet yield, platelet dose, and GF dose showed a positive correlation with PRP platelet count. Age, gender, and platelet indices had no significant influence.

Abstract Background The liver is the second most injured organ following blunt abdominal trauma (BAT) after the spleen. Although the computed tomography (CT) scan is considered as the gold standard for diagnosing liver injury in BAT, it may not readily available in all the hospitals. This study was performed to evaluate the role of aspartate transaminase (AST) and alanine transaminase (ALT) in patients with BAT and its significance in predicting the diagnosis and severity of the liver injury. Method The study was conducted in Chitwan Medical College Teaching Hospital (CMCTH) from February 2019 to May 2020. It was a prospective observational study. All the patients with BAT were received by on-duty surgical residents in the emergency department. Based on the imaging and operative finding, patients with liver injury and without liver injury were noted with the associated injury. For comparisons of clinical and grading characteristics between the two groups (liver injury and no liver injury), the chi-squared test was used for categorical variables as appropriate, and the Mann-Whitney U test used for quantitative variables (AST and ALT). The comparisons between more than two groups (grade of injury) were performed using the Kruskal-Wallis test. The receiver operating characteristic (ROC) was used to calculate the optimal cut-off value of AST and ALT. Results Among the 96 patients admitted with BAT, 38 patients had liver injury and 58 patients had no liver injury. The median length of the intensive care unit (ICU) stay of patients with liver injury was higher than without liver injury. There was a significant difference in the median level of AST and ALT (

Outbreaks of highly pathogenic strains of avian influenza (HPAI) cause high mortality in avian populations worldwide. When spread from avian reservoirs to humans, HPAI infections cause mortality in about 50% of human infections. Cases of human-to-human transmission of HPAI are relatively rare, and have, to date, only been reported in situations of close contact. These transmissions have resulted in isolated clusters of human HPAI infections, but have not yet caused a pandemic. Given the large number of human H5N1 HPAI infections to date, none of which has resulted in a pandemic, we estimate an upper bound on the probability of H5N1 pandemic emergence. We use this estimate to provide the likelihood of observing such a pandemic over the next decade. We then develop a more accurate parameter-based estimate of the emergence probability and predict the likelihood that, through rare mutations, an H5N1 influenza pandemic will emerge over the same time span.

Introduction: In the current era, laparoscopic cholecystectomy is the treatment of choice for symptomatic gallstone disease. The aim of this study is to find out the prevalence of open cholecystectomy among patients undergoing laparoscopic cholecystectomy in a tertiary care centre. Methods: It is a descriptive cross-sectional study done among 345 patients at the Department of Surgery of a tertiary care centre from June, 2020 to May, 2021 after receiving ethical clearance from the Institutional Review Committee (Reference number: CMC-IRC/0770798-271). Convenience sampling was done. Successive patients who underwent elective laparoscopic cholecystectomy during the study period were included. Standard 4 port laparoscopic technique was used for the laparoscopic cholecystectomy and sub-costal Kocher incision was used for the open cholecystectomy respectively. After data collection, entry and analysis were done in Microsoft Excel. Point estimate at a 95% Confidence Interval was calculated along with frequency and percentages for binary data. Results: Out of 345 patients, the prevalence of open cholecystectomy among patients undergoing laparoscopic cholecystectomy was 6 (1.73%) (0.35-3.11 at a 95% Confidence Interval). Conclusions: This study showed that the prevalence of open cholecystectomy among patients undergoing laparoscopic cholecystectomy was lower when compared to similar studies conducted in similar settings.

Highly efficient and durable, silver nanoparticles doped Active Carbon Spheres ACS(Ag) were synthesized by carbonization and activation of silver exchanged resins. The silver exchanged resins were prepared by exchanging H+ ions of polystyrene sulphonate resin with Ag+ ions of silver nitrate (AgNO3). The quantity of Ag+ in the spheres was controlled by varying the concentration of AgNO3, from 0.0125 to 0.1 M. With increasing molar concentration of AgNO3, the effective intake of Ag+ by the sphere increases from 1.1 to 8.1 weight percent (wt %). For activation, the spheres were incubated in the CO2 atmosphere for 6 h at fixed soaking temperature i.e. 1123 K. The characterization of synthesized silver doped ACS was performed by using different sophisticated instrumental techniques. The antimicrobial activity of silver doped ACS was studied against different bacterial strains like, E. coli, B. subtilis and Staphylococcus aureus. The study demonstrated that the zone of inhibition for E. coli was 16.9 ± 0.7 mm while for B. subtilis it was 17.1 ± 0.3 mm at a concentration of 8 mg of synthesized material. In addition, satisfactory results were obtained in shake flask and filtration test experiments also, even at a low concentration of 2 mg, showing growth inhibition of 94% for E. coli and 93% for B. subtilis. When the concentration of silver doped ACS was increased to 8 mg, complete removal of both the bacteria was observed after 24 h (100 % reduction for E. coli and B. subtilis). Furthermore, when silver doped ACS was tested against Staphylococcus aureus according to ASTM:E 2149-01 method, biocidal activity of up to 73% was observed. Therefore, the silver doped ACS can be considered as a potential biocidal material for the studied bacterial strains and hence find suitable application for decontamination of water.

Abstract The effect of Plant Growth Promoting Rhizobacteria (Bacillus sp.) and silver nanoparticles on Zea mays was evaluated. The silver nanoparticles were synthesized from Tagetes erecta (Marigold) leaf and flower extracts, whereas PGPR isolated from spinach rhizosphere. The silver nanoparticles (AgNPs) were purified using ultra centrifugation and were characterized using UV–Vis spectroscopy at gradient wavelength and also by High Resolution Transmission Electron microscopy (HRTEM). The average particles size of AgNPs was recorded approximately 60 nm. Almost all potential isolates were able to produce Indole Acetic Acid (IAA), ammonia and Hydrogen cyanide (HCN), solubilized tricalcium phosphate and inhibited the growth of Macrophomina phaseolina in vitro but the isolate LPR2 was found the best among all. On the basis of 16S rRNA gene sequence, the isolate LPR2 was characterized as Bacillus cereus LPR2. The maize seeds bacterized with LPR2 and AgNPs individually showed a significant increase in germination (87.5%) followed by LPR2 + AgNPs (75%). But the maximum growth of root and shoot of maize plant was observed in seeds coated with LPR2 followed by AgNPs and a combination of both. Bacillus cereus LPR2 and silver nanoparticles enhanced the plant growth and LPR2 strongly inhibited the growth of deleterious fungal pathogen. Therefore, LPR2 and AgNPs could be utilized as bioinoculant and growth stimulator, respectively for maize.

Context: Cordia sebestena fruits are traditionally used to treat wounds, boils, tumors, gout, ulcer, flu, fever, asthma, menstrual cramps, dysentery, diarrhea, headache, snakebite and liver disorders. However, information on hepatoprotective potential of Cordia sebestena fruit has not been reported in the research. Aims: To evaluate the hepatoprotective effect of the ethanolic extract of Cordia sebestena fruit (CSFE) against simvastatin-induced hepatotoxicity in rats. Methods: After authentication of fruit, its ethanolic extract was collected. Hepatotoxicity was induced by simvastatin in rodents. Hepatoprotective potential of CSFE was evaluated at 200 and 400 mg/kg, body weight by determining the altered levels of biochemical parameters like serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), cholesterol, bilirubin, urea, albumin, total protein and hematological indices including red blood cells (RBC), white blood cells (WBC), hemoglobin (Hb), platelets, and lymphocytes along with the impact on body and liver weight of treated rats. Results: The treatment with CSFE at 200 mg/kg and 400 mg/kg, significantly at (p

Introduction The diagnosis of anemia in pregnancy should be early to prevent complications to mother and baby. It should be simple and safe, and therefore feasibility of reticulocyte parameters may prove beneficial in this regard. Newer reticulocyte parameters have not been studied extensively especially in pregnancy. The present study was conducted to evaluate reticulocyte hemoglobin content (Ret-He), immature reticulocyte fraction, and reticulocyte count and to compare them with serum ferritin, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) in assessment anemia in pregnancy. Material and Methods Prospective study included first trimester pregnant females where hemoglobin, MCV, and MCH were categorized and statistically analyzed with serum ferritin and reticulocyte parameters. Results The study included 155 first trimester pregnant females showing statistical difference in Ret-He between all categories of hemoglobin and MCV (p < 0.05). ROC curve analysis using MCV < 83 fl and MCH

Widespread forest fire events occurred in the foothills of North Western Himalaya during 24 April to 2 May 2016 (Event-1) and 20–30 May 2018 (Event-2). Their impacts were investigated on the distribution of pollutant gases ozone (O3), carbon monoxide (CO), and oxides of nitrogen (NOx) over Uttarakhand using simulations of Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and in-situ observations of these gases over Dehradun, the capital of Uttarakhand. During Event-1, the observed CO mixing ratio over Dehradun increased from 25 April 2016 onwards, attained maximum (705.8 ± 258 ppbv) on 2 May 2016 and subsequently decreased. The rate of increase of daily baseline CO was 29 ppbv/day during HFAP (High Fire Activity Period). During Event-2, daily average concentrations of CO, O3, and NOx showed systematic increase over Dehradun during HFAP period. The rate of increase of CO was 9 ppbv/day, while it was very small for NOx and O3. To quantitatively estimate the influence of forest fire emissions, two WRF-Chem simulations were made: one with biomass burning (BB) emissions and other without BB emissions. These simulations showed 52% (34%) enhancement in CO, 52% (32%) enhancement in NOx, and 11% (9%) enhancement in O3 during HFAP for Event-1 (Event-2). A clear positive correlation (r = 0.89 for Event-1, r = 0.69 for Event-2) was found between ∆O3 (O3with BB minus O3without BB) and ∆CO (COwith BB minus COwithout BB), indicating rapid production of ozone in the fire plumes. For both the events, the vertical distribution of ∆O3, ∆CO, and ∆NOx showed that forest fire emissions influenced the air quality upto 6.5 km altitude. Peaks in ∆O3, ∆CO, and ∆NOx during different days suggested the role of varying dispersion and horizontal mixing of fire plumes.

Background and Aims: Breast milk is the only food for the neonate with vast benefits. Although breastfeeding process is natural but extensive research has revealed that mother requires active support for establishing and sustaining appropriate breast feeding practices. Due to high patient load, effective counselling of all pregnant ladies becomes a huge task. Video-assisted counselling has been tried for establishing and sustaining breastfeeding practices as it helps mothers to understand the procedure and process better as the video image is animated with a narrative voice. The objective is to study the effectiveness of video assisted breast feeding counselling in establishing and sustaining breast feeding practices. Materials and Methods: A Quasi experimental study was done in Obstetrics and Gynaecology department of BRD Medical College Gorakhpur from September 2019 to February 2020. Study subjects were pregnant ladies admitted in labour room for delivery. A total of 60 study subjects were allocated in Video-assisted breast feeding counselling group and same number in routine counselling group. Video-assisted counselling group breast feeding counselling was done with use of two videos (Video 1 is of 5 minute and 34 seconds duration and Video 2 is of 2 minutes and 50 seconds duration) demonstrations in labour room and post natal ward. Routine breast feeding counselling group received routine counselling as done by resident or consultants during their care. After the delivery the study participants were interviewed regarding their profile and breast feeding knowledge, motivation and behaviour and a scoring was done with Min. -0 to Max. 20 score. Result: Higher proportion of early initiation of breast feeding was observed in the video assisted counselling group as compared with the routine counselling group. The mean knowledge score of the subjects' video-assisted counselling group the mean knowledge score was significantly higher. Motivation of mother for breast feeding and improvement in their behaviour and skill in video-assisted counselling group was higher in comparison to routine counselling group. Conclusion: Video-assisted counselling was found effective in successful initiation and establishment of breast feeding.